Evolutionary dynamics of cooperation in neutral populations

Cooperation is a difficult proposition in the face of Darwinian selection. Those that defect have an evolutionary advantage over cooperators who should therefore die out. However, spatial structure enables cooperators to survive through the formation of homogeneous clusters, which is the hallmark of network reciprocity. Here we go beyond this traditional setup and study the spatiotemporal dynamics of cooperation in a population of populations. We use the prisoner's dilemma game as the mathematical model and show that considering several populations simultaneously give rise to fascinating spatiotemporal dynamics and pattern formation. Even the simplest assumption that strategies between different populations are payoff-neutral with one another results in the spontaneous emergence of cyclic dominance, where defectors of one population become prey of cooperators in the other population, and vice versa. Moreover, if social interactions within different populations are characterized by significantly different temptations to defect, we observe that defectors in the population with the largest temptation counterintuitively vanish the fastest, while cooperators that hang on eventually take over the whole available space. Our results reveal that considering the simultaneous presence of different populations significantly expands the complexity of evolutionary dynamics in structured populations, and it allow us to understand the stability of cooperation under adverse conditions that could never be bridged by network reciprocity alone.Comment: 14 pages, 7 figures; accepted for publication in New Journal of Physic

Modelling the Selection of Waiting Areas on Subway Platforms Based on the Bacterial Chemotaxis Algorithm

Based on the bacterial chemotaxis algorithm, a new waiting-area selection model (WASM) is proposed that predicts well the pedestrian distribution in subway waiting areas. WASM regards passengers waiting on a subway platform as two-dimensional points and adopts an essential rejection factor to determine the target waiting area. Based on WASM, three experiments were carried out to explore how passenger volume, waiting-area capacity, and staircase position affect the number and distribution of waiting passengers. The experimental results show the following. 1) Regardless of the passenger flow, passengers prefer waiting areas that are between the stairs. 2) Setting proper capacity limits on waiting areas can help to improve subway transportation efficiency when passenger flow is relatively high. 3) The experimental results show that the closer the staircases, the more passengers are left stranded on the platform

Point-actuated feedback control of multidimensional interfaces

We consider the application of feedback control strategies with point actuators to stabilise desired interface shapes. We take a multidimensional Kuramoto--Sivashinsky equation as a test case; this equation arises in the study of thin liquid films, exhibiting a wide range of dynamics in different parameter regimes, including unbounded growth and full spatiotemporal chaos. In the case of limited observability, we utilise a proportional control strategy where forcing at a point depends only on the local observation. We find that point-actuated controls may inhibit unbounded growth of a solution, if they are sufficient in number and in strength, and can exponentially stabilise the desired state. We investigate actuator arrangements, and find that the equidistant case is optimal, with heavy penalties for poorly arranged actuators. We additionally consider the problem of synchronising two chaotic solutions using proportional controls. In the case when the full interface is observable, we construct feedback gain matrices using the linearised dynamics. Such controls improve on the proportional case, and are applied to stabilise non-trivial steady and travelling wave solutions.Comment: 28 page

Globalization and the rise and fall of cognitive control

This is the final published version, available from Nature Research via the DOI in this recordData availability: All data required to run the simulations are available at: https://osf.io/fy94w/ or can be requested from the authors. A reporting summary for this Article is available as a Supplementary Information file.Code availability: All scripts necessary to reproduce the results are available at: https://osf.io/fy94w/ or can be requested from the authors.The scale of human interaction is larger than ever before—people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive) versus controlled (e.g., deliberative) cognition, interact with each other, and influence the environment (i.e., game payoffs). We find that globalized direct contact between agents can either favor or disfavor control, depending on whether controlled agents are harmed or helped by contact with automatic agents; globalized environment disfavors cognitive control, while also promoting strategic diversity and fostering mesoscale communities of more versus less controlled agents; and globalized learning destroys mesoscale communities and homogenizes the population. These results emphasize the importance of the scale of interaction for the evolution of cognition, and help shed light on modern challenges.Ethics and Governance of Artificial Intelligence Initiative of the Miami FoundationWilliam and Flora Hewlett FoundationJohn Templeton FoundationSocial Sciences and Humanities Research Council of Canad